AI Text Generators and Text Producers

Henrik Køhler Simonsen
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引用次数: 1

Abstract

AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.
AI文本生成器和文本生成器
人工智能生成的文本在许多行业中变得越来越重要,它已经给我们编写文本和生成内容的方式带来了巨大的变化。本文利用了一项涉及70名测试对象的描述性分析研究的经验数据。总共有115名测试者,他们收到了一封带有说明的电子邮件。70名测试对象参加了这项研究。首先,测试对象被要求测试一个特定的AI文本生成器(ATG),并使用相同的语言内容进行三次提示操作。其次,在测试了ATG后,测试对象被要求参与一份问卷,其中有十个问题,重点是他们如何体验ATG的表现以及他们如何使用ATG。大多数测试对象发现,测试的ATG在生成文本时很容易使用。当被问及人工智能生成内容的感知质量时,受访者对质量印象不深,并表示他们需要进行几次编辑操作。数据还表明,atg在之前、期间和之后都需要帮助。本文提出了一个三阶段编辑框架,可用于atg的使用和教学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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